





"""simple docstring"""





import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[str]				,		SCREAMING_SNAKE_CASE	:		Union[str, Any]				,		SCREAMING_SNAKE_CASE	:		List[str]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			MobileBertConfig.from_json_file(SCREAMING_SNAKE_CASE     )
      print(F'Building PyTorch model from configuration: {config}'     )
      lowerCAmelCase    		=			MobileBertForPreTraining(SCREAMING_SNAKE_CASE     )
      # Load weights from tf checkpoint
      lowerCAmelCase    		=			load_tf_weights_in_mobilebert(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      # Save pytorch-model
      print(F'Save PyTorch model to {pytorch_dump_path}'     )
      torch.save(model.state_dict()				,		SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--mobilebert_config_file",
           default=None,
           type=str,
           required=True,
           help=(
               "The config json file corresponding to the pre-trained MobileBERT model. \n"
               "This specifies the model architecture."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)













"""simple docstring"""





from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE__   =  300  # TEMPERATURE (unit = K)


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		float				,		SCREAMING_SNAKE_CASE	:		float				,		SCREAMING_SNAKE_CASE	:		float				,		):







      '''simple docstring'''




      if donor_conc <= 0:
            raise ValueError("""Donor concentration should be positive"""     )
      elif acceptor_conc <= 0:
            raise ValueError("""Acceptor concentration should be positive"""     )
      elif intrinsic_conc <= 0:
            raise ValueError("""Intrinsic concentration should be positive"""     )
      elif donor_conc <= intrinsic_conc:
            raise ValueError(
                """Donor concentration should be greater than intrinsic concentration"""     )
      elif acceptor_conc <= intrinsic_conc:
            raise ValueError(
                """Acceptor concentration should be greater than intrinsic concentration"""     )
      else:
            return (
                Boltzmann
                * T
                * log((donor_conc * acceptor_conc) / intrinsic_conc**2     )
                / physical_constants["electron volt"][0]
            )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






